EFFECTIVENESS OF THE LACEY ASSESSMENT OF PRETERM INFANTS TO PREDICT NEUROMOTOR OUTCOMES FOR PREMATURE BABIES AT TWELVE MONTHS CORRECTED AGE
Bibliographic record
Abstract
Background: The Lacey Assessment of Preterm Infants (LAPI) is used in clinical practice to identify premature babies at risk of neuromotor impairments, especially cerebral palsy. There is a shortage of studies on the Lacey assessment despite its wide clinical use. This study attempted to find the diagnostic accuracy of the Lacey assessment of preterm infants to predict neuromotor outcomes of premature babies at 12 months corrected age and to compare their predictive ability with brain ultrasound.Methods: This prospective cohort study included 89 preterm infants (45 females & 44 males) born below 35 weeks gestation. An initial assessment was done using the Lacey Assessment of Preterm Infants (LAPI) after babies reached 33 weeks postmenstrual age. Follow up assessment on neuromotor outcomes was done at 12 months (±1 week) corrected age using two standardized outcome measures, i.e., Infant Neurological International Battery and Alberta Infant Motor Scale. Brain ultrasound data were collected retrospectively. Data were statistically analyzed, and the diagnostic accuracy of the Lacey Assessment of Preterm Infants (LAPI) alone and in combination with brain ultrasound was calculated.Results: Fisher's exact test showed p<.01, indicating that there is an association between the Lacey Assessment of Preterm Infants (LAPI) and the neuromotor outcomes at one year corrected age. A combination of Lacey Assessment (LAPI) and brain ultrasound results showed higher sensitivity in predicting abnormal neuromotor outcomes than Lacey Assessment alone (80% vs. 66.7%, respectively). Lacey Assessment also showed high specificity (96.3%) and negative predictive value (97.5%).Conclusion: Results of this study suggest that the Lacey Assessment of Preterm Infants (LAPI) can be used as a supplementary assessment tool for premature babies to identify those at risk of abnormal neuromotor outcomes. These findings have applications to identify premature babies eligible for early intervention services.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".